Mining optimized association rules for numeric attributes

Abstract

This study is aimed at realizing a system that automatically finds appropriate ranges for numeric attributes in a given huge database. The study mainly focuses on computing two optimized ranges: one that maximizes the support on the condition that the confidence ratio is at least a given threshold value, and another that maximizes the confidence ratio on the condition that the support is at least a given threshold number. Using techniques from computational geometry, presented are novel algorithms that compute the optimized ranges in linear time if the data are sorted. Tests show the implementation to be fast not only in theory but also in practice.